Large corporate enterprises generate enormous amounts of data. Data is generated in documents of all kinds, electronic mail messages, instant messages, and other types of communications. Due to the large quantity of data generated and the fact that the data is often stored in many disparate locations, it can be very difficult for an individual seeking data (a “seeker”) to locate information of interest. This is due, in part, to the fact that the data is frequently not linked, such as with documents made available on the World Wide Web (the “Web”).
Employees working within large corporate enterprises are also typically experts on a large number of topics both related an unrelated to the business of the enterprise. An expert is an individual that is likely to help a seeker locate information relating to a topic or to locate another person that may have information related to the topic. In addition to seeking information, a seeker may also be interested identifying and contacting an expert on a topic of interest. However, it is very difficult to determine who the experts are for a particular topic within a large enterprise that may include tens or even hundreds of thousands of employees.
Systems do exist for assisting in the process of locating an expert within a large enterprise. However, current systems suffer from a number of serious drawbacks that limit their usefulness. In particular, current systems tend to locate too few experts within a large population. As a result, the limited number of experts may be overwhelmed with a large number of requests for assistance. As a consequence of the large number of requests, the experts frequently withdraw from the expert program. This results in the system having even fewer experts and the problem is exacerbated.
It is with respect to these considerations and others that the various embodiments of the present invention have been made.